Seam carving is a technique that’s been fundamental to image processing in the last decade. This technique allows content-aware modifications to an image’s size. The key part of “content-aware” is that it allows a path to modifying an image without affecting important parts of the image. These ‘seams’ are 1 pixel wide paths that run from one side of an image to another according to a chosen energy function [1].
However, there is no research into resizing high frequency content in images, which is a weakness of standard seam carving methods. Some images are defined by the regularity of their recognizable content, sometimes referred to as ‘textons’, and so modifying them becomes very apparent to the observer’s brain[2]. The seam carving method assumes that important content is largely separated, and that removing seams is statistically unlikely to affect vital image content. This assumption obviously breaks down once you have repeated or regular content, since the seams are unidirectional they are likely to collide with many important sections of the image, thus the changes are easily perceptible[3].
This problem is an important one for texture-mapping onto planes or 3D models, because of the overhead in handling surfaces with different aspect ratios than the one source image. Simple methods involve scaling or cropping the source image, other methods are to create many different textures for different aspect ratios (via a tool like photoshop). These are not ideal since they are fundamentally not suited for this task, or are too much extra work, poor scalability, and storage footprint for the multiple-images approach.
The goal of this project is to outline an approach for resizing images with a high frequency of textons.